生物医学信号处理第一次上机

filter myopen2520151050-5-10-15-20-25010002000300040005000600070008000?

? ? ? ? ?

y=filter(D,C,my_open); plot(my_open); hold on plot(y,'y')

title(' filtfilt. my_open')

filtfilt. myopen6040200-20-40-60010002000300040005000600070008000y=filter(D,C,my_close); plot(my_close); hold on plot(y,'y')

2520151050-5-10-15-20-25010002000300040005000600070008000

Question 4, state the difference between two filter methods. In your lab report, include a picture of both new signals.

Now you have two new signals for both conditions, respectively.

? 5, Power Spectral Density (PSD) estimate via periodogram method and Welch's

method to analyze four new signals. (if you have trouble in this step, see cue 3)

? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?

?

fs=250; Rp=1;Rs=50;

Wp=[2*4/fs 2*8/fs];Ws=[2*2/fs 2*10/fs]; [N, Wn] = ellipord(Wp, Ws, Rp, Rs); [B,A] = ellip(N,Rp,Rs,Wn); freqz(B,A)

y=filter(B,A,my_open); plot(my_open); hold on plot(y,'r') Fs = 1000; t = 0:1/Fs:1; xn=y figure(1)

periodogram(xn,[],[],Fs); %周期图法 figure(2)

pwelch(xn,[],[],[],Fs);%welch方法

?

Power Spectral Density Estimate via Periodogram100-10Power/frequency (dB/Hz)-20-30-40-50-60-7000.050.10.150.20.25Frequency (kHz)0.30.350.40.450.5

Power Spectral Density Estimate via Welch100-10-20Power/frequency (dB/Hz)-30-40-50-60-70-8000.050.10.150.20.25Frequency (kHz)0.30.350.40.450.5

代码2：4~8hz滤波器实现

fs=250; Rp=1;Rs=50;

Wp=[2*4/fs 2*8/fs];Ws=[2*2/fs 2*10/fs];